Overview

Brought to you by YData

Dataset statistics

Number of variables20
Number of observations131914
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory21.1 MiB
Average record size in memory168.0 B

Variable types

Numeric8
Text4
Categorical6
Boolean2

Alerts

Academic Pressure is highly overall correlated with Age and 6 other fieldsHigh correlation
Age is highly overall correlated with Academic Pressure and 4 other fieldsHigh correlation
CGPA is highly overall correlated with Academic Pressure and 6 other fieldsHigh correlation
Depression is highly overall correlated with Academic Pressure and 6 other fieldsHigh correlation
Job Satisfaction is highly overall correlated with Academic Pressure and 4 other fieldsHigh correlation
Study Satisfaction is highly overall correlated with Academic Pressure and 6 other fieldsHigh correlation
Work Pressure is highly overall correlated with Academic Pressure and 4 other fieldsHigh correlation
Working Professional or Student is highly overall correlated with Academic Pressure and 6 other fieldsHigh correlation
Sleep Duration is highly imbalanced (60.9%) Imbalance
Dietary Habits is highly imbalanced (64.9%) Imbalance
id is uniformly distributed Uniform
id has unique values Unique
Academic Pressure has 104032 (78.9%) zeros Zeros
Work Pressure has 27882 (21.1%) zeros Zeros
Study Satisfaction has 104032 (78.9%) zeros Zeros
Job Satisfaction has 27882 (21.1%) zeros Zeros
Work/Study Hours has 11331 (8.6%) zeros Zeros

Reproduction

Analysis started2024-12-07 15:24:50.120019
Analysis finished2024-12-07 15:25:00.982284
Duration10.86 seconds
Software versionydata-profiling vv4.11.0
Download configurationconfig.json

Variables

id
Real number (ℝ)

Uniform  Unique 

Distinct131914
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70370.272
Minimum0
Maximum140699
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2024-12-07T22:25:01.053486image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7071.65
Q135168.25
median70374.5
Q3105568.75
95-th percentile133658.35
Maximum140699
Range140699
Interquartile range (IQR)70400.5

Descriptive statistics

Standard deviation40612.885
Coefficient of variation (CV)0.57713127
Kurtosis-1.2009313
Mean70370.272
Median Absolute Deviation (MAD)35200.5
Skewness-0.00067428574
Sum9.282824 × 109
Variance1.6494064 × 109
MonotonicityStrictly increasing
2024-12-07T22:25:01.154080image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
93896 1
 
< 0.1%
93861 1
 
< 0.1%
93860 1
 
< 0.1%
93858 1
 
< 0.1%
93857 1
 
< 0.1%
93856 1
 
< 0.1%
93855 1
 
< 0.1%
93854 1
 
< 0.1%
93853 1
 
< 0.1%
Other values (131904) 131904
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
140699 1
< 0.1%
140698 1
< 0.1%
140697 1
< 0.1%
140696 1
< 0.1%
140694 1
< 0.1%
140693 1
< 0.1%
140692 1
< 0.1%
140691 1
< 0.1%
140690 1
< 0.1%
140689 1
< 0.1%

Name
Text

Distinct414
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
2024-12-07T22:25:01.504064image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length14
Median length11
Mean length5.6394014
Min length2

Characters and Unicode

Total characters743916
Distinct characters49
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique174 ?
Unique (%)0.1%

Sample

1st rowAaradhya
2nd rowVivan
3rd rowYuvraj
4th rowYuvraj
5th rowRhea
ValueCountFrequency (%)
rohan 3047
 
2.3%
aarav 2211
 
1.7%
rupak 2112
 
1.6%
anvi 1892
 
1.4%
aaradhya 1854
 
1.4%
raghavendra 1828
 
1.4%
vani 1602
 
1.2%
ritvik 1539
 
1.2%
tushar 1522
 
1.2%
shiv 1520
 
1.2%
Other values (405) 112789
85.5%
2024-12-07T22:25:01.971099image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 152079
20.4%
i 77327
 
10.4%
h 69274
 
9.3%
n 50378
 
6.8%
r 45592
 
6.1%
s 33321
 
4.5%
A 31266
 
4.2%
v 29838
 
4.0%
R 22480
 
3.0%
y 20587
 
2.8%
Other values (39) 211774
28.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 743916
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 152079
20.4%
i 77327
 
10.4%
h 69274
 
9.3%
n 50378
 
6.8%
r 45592
 
6.1%
s 33321
 
4.5%
A 31266
 
4.2%
v 29838
 
4.0%
R 22480
 
3.0%
y 20587
 
2.8%
Other values (39) 211774
28.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 743916
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 152079
20.4%
i 77327
 
10.4%
h 69274
 
9.3%
n 50378
 
6.8%
r 45592
 
6.1%
s 33321
 
4.5%
A 31266
 
4.2%
v 29838
 
4.0%
R 22480
 
3.0%
y 20587
 
2.8%
Other values (39) 211774
28.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 743916
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 152079
20.4%
i 77327
 
10.4%
h 69274
 
9.3%
n 50378
 
6.8%
r 45592
 
6.1%
s 33321
 
4.5%
A 31266
 
4.2%
v 29838
 
4.0%
R 22480
 
3.0%
y 20587
 
2.8%
Other values (39) 211774
28.5%

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
Male
72578 
Female
59336 

Length

Max length6
Median length4
Mean length4.8996164
Min length4

Characters and Unicode

Total characters646328
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowMale
3rd rowMale
4th rowMale
5th rowFemale

Common Values

ValueCountFrequency (%)
Male 72578
55.0%
Female 59336
45.0%

Length

2024-12-07T22:25:02.117461image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-07T22:25:02.225300image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
male 72578
55.0%
female 59336
45.0%

Most occurring characters

ValueCountFrequency (%)
e 191250
29.6%
a 131914
20.4%
l 131914
20.4%
M 72578
 
11.2%
F 59336
 
9.2%
m 59336
 
9.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 646328
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 191250
29.6%
a 131914
20.4%
l 131914
20.4%
M 72578
 
11.2%
F 59336
 
9.2%
m 59336
 
9.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 646328
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 191250
29.6%
a 131914
20.4%
l 131914
20.4%
M 72578
 
11.2%
F 59336
 
9.2%
m 59336
 
9.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 646328
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 191250
29.6%
a 131914
20.4%
l 131914
20.4%
M 72578
 
11.2%
F 59336
 
9.2%
m 59336
 
9.2%

Age
Real number (ℝ)

High correlation 

Distinct43
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.088497
Minimum18
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2024-12-07T22:25:02.392118image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile21
Q131
median42
Q351
95-th percentile58
Maximum60
Range42
Interquartile range (IQR)20

Descriptive statistics

Standard deviation11.96515
Coefficient of variation (CV)0.29120436
Kurtosis-1.118527
Mean41.088497
Median Absolute Deviation (MAD)10
Skewness-0.23431073
Sum5420148
Variance143.1648
MonotonicityNot monotonic
2024-12-07T22:25:02.572476image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
56 5101
 
3.9%
49 4929
 
3.7%
53 4454
 
3.4%
38 4254
 
3.2%
57 4241
 
3.2%
47 4081
 
3.1%
46 4004
 
3.0%
54 3861
 
2.9%
51 3790
 
2.9%
43 3691
 
2.8%
Other values (33) 89508
67.9%
ValueCountFrequency (%)
18 1614
1.2%
19 1581
1.2%
20 2263
1.7%
21 2642
2.0%
22 2023
1.5%
23 2842
2.2%
24 3266
2.5%
25 2879
2.2%
26 2067
1.6%
27 2550
1.9%
ValueCountFrequency (%)
60 2368
1.8%
59 3688
2.8%
58 2839
2.2%
57 4241
3.2%
56 5101
3.9%
55 2792
2.1%
54 3861
2.9%
53 4454
3.4%
52 2569
1.9%
51 3790
2.9%

City
Text

Distinct97
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
2024-12-07T22:25:02.791730image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length18
Median length11
Mean length7.0157224
Min length2

Characters and Unicode

Total characters925472
Distinct characters49
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique51 ?
Unique (%)< 0.1%

Sample

1st rowLudhiana
2nd rowVaranasi
3rd rowVisakhapatnam
4th rowMumbai
5th rowKanpur
ValueCountFrequency (%)
kalyan 6295
 
4.8%
patna 5662
 
4.3%
vasai-virar 5387
 
4.1%
kolkata 5327
 
4.0%
meerut 5270
 
4.0%
ahmedabad 5096
 
3.9%
visakhapatnam 4927
 
3.7%
pune 4905
 
3.7%
ludhiana 4834
 
3.7%
rajkot 4828
 
3.7%
Other values (88) 79387
60.2%
2024-12-07T22:25:03.168746image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 205414
22.2%
r 70758
 
7.6%
n 63643
 
6.9%
i 55181
 
6.0%
d 45763
 
4.9%
e 44087
 
4.8%
u 40443
 
4.4%
h 35587
 
3.8%
t 30435
 
3.3%
o 29908
 
3.2%
Other values (39) 304253
32.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 925472
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 205414
22.2%
r 70758
 
7.6%
n 63643
 
6.9%
i 55181
 
6.0%
d 45763
 
4.9%
e 44087
 
4.8%
u 40443
 
4.4%
h 35587
 
3.8%
t 30435
 
3.3%
o 29908
 
3.2%
Other values (39) 304253
32.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 925472
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 205414
22.2%
r 70758
 
7.6%
n 63643
 
6.9%
i 55181
 
6.0%
d 45763
 
4.9%
e 44087
 
4.8%
u 40443
 
4.4%
h 35587
 
3.8%
t 30435
 
3.3%
o 29908
 
3.2%
Other values (39) 304253
32.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 925472
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 205414
22.2%
r 70758
 
7.6%
n 63643
 
6.9%
i 55181
 
6.0%
d 45763
 
4.9%
e 44087
 
4.8%
u 40443
 
4.4%
h 35587
 
3.8%
t 30435
 
3.3%
o 29908
 
3.2%
Other values (39) 304253
32.9%

Working Professional or Student
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
Working Professional
104032 
Student
27882 

Length

Max length20
Median length20
Mean length17.252255
Min length7

Characters and Unicode

Total characters2275814
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWorking Professional
2nd rowWorking Professional
3rd rowStudent
4th rowWorking Professional
5th rowWorking Professional

Common Values

ValueCountFrequency (%)
Working Professional 104032
78.9%
Student 27882
 
21.1%

Length

2024-12-07T22:25:03.285308image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-07T22:25:03.370444image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
working 104032
44.1%
professional 104032
44.1%
student 27882
 
11.8%

Most occurring characters

ValueCountFrequency (%)
o 312096
13.7%
n 235946
10.4%
r 208064
 
9.1%
i 208064
 
9.1%
s 208064
 
9.1%
e 131914
 
5.8%
W 104032
 
4.6%
l 104032
 
4.6%
a 104032
 
4.6%
f 104032
 
4.6%
Other values (8) 555538
24.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2275814
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 312096
13.7%
n 235946
10.4%
r 208064
 
9.1%
i 208064
 
9.1%
s 208064
 
9.1%
e 131914
 
5.8%
W 104032
 
4.6%
l 104032
 
4.6%
a 104032
 
4.6%
f 104032
 
4.6%
Other values (8) 555538
24.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2275814
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 312096
13.7%
n 235946
10.4%
r 208064
 
9.1%
i 208064
 
9.1%
s 208064
 
9.1%
e 131914
 
5.8%
W 104032
 
4.6%
l 104032
 
4.6%
a 104032
 
4.6%
f 104032
 
4.6%
Other values (8) 555538
24.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2275814
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 312096
13.7%
n 235946
10.4%
r 208064
 
9.1%
i 208064
 
9.1%
s 208064
 
9.1%
e 131914
 
5.8%
W 104032
 
4.6%
l 104032
 
4.6%
a 104032
 
4.6%
f 104032
 
4.6%
Other values (8) 555538
24.4%
Distinct64
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
2024-12-07T22:25:03.597601image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length22
Median length20
Mean length9.9084555
Min length2

Characters and Unicode

Total characters1307064
Distinct characters49
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)< 0.1%

Sample

1st rowChef
2nd rowTeacher
3rd rowStudent
4th rowTeacher
5th rowBusiness Analyst
ValueCountFrequency (%)
student 27886
16.2%
teacher 24900
 
14.5%
consultant 8940
 
5.2%
content 7811
 
4.5%
writer 7811
 
4.5%
manager 7735
 
4.5%
analyst 6755
 
3.9%
architect 4362
 
2.5%
engineer 4154
 
2.4%
hr 4022
 
2.3%
Other values (63) 67530
39.3%
2024-12-07T22:25:03.963745image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 172571
13.2%
t 154700
 
11.8%
n 116874
 
8.9%
a 103366
 
7.9%
r 100081
 
7.7%
c 63038
 
4.8%
i 59219
 
4.5%
u 56455
 
4.3%
h 44343
 
3.4%
s 44257
 
3.4%
Other values (39) 392160
30.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1307064
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 172571
13.2%
t 154700
 
11.8%
n 116874
 
8.9%
a 103366
 
7.9%
r 100081
 
7.7%
c 63038
 
4.8%
i 59219
 
4.5%
u 56455
 
4.3%
h 44343
 
3.4%
s 44257
 
3.4%
Other values (39) 392160
30.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1307064
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 172571
13.2%
t 154700
 
11.8%
n 116874
 
8.9%
a 103366
 
7.9%
r 100081
 
7.7%
c 63038
 
4.8%
i 59219
 
4.5%
u 56455
 
4.3%
h 44343
 
3.4%
s 44257
 
3.4%
Other values (39) 392160
30.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1307064
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 172571
13.2%
t 154700
 
11.8%
n 116874
 
8.9%
a 103366
 
7.9%
r 100081
 
7.7%
c 63038
 
4.8%
i 59219
 
4.5%
u 56455
 
4.3%
h 44343
 
3.4%
s 44257
 
3.4%
Other values (39) 392160
30.0%

Academic Pressure
Real number (ℝ)

High correlation  Zeros 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.66415998
Minimum0
Maximum5
Zeros104032
Zeros (%)78.9%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2024-12-07T22:25:04.074446image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.4313107
Coefficient of variation (CV)2.1550692
Kurtosis2.6320172
Mean0.66415998
Median Absolute Deviation (MAD)0
Skewness2.0139949
Sum87612
Variance2.0486504
MonotonicityNot monotonic
2024-12-07T22:25:04.160477image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 104032
78.9%
3 7460
 
5.7%
5 6293
 
4.8%
4 5154
 
3.9%
1 4799
 
3.6%
2 4176
 
3.2%
ValueCountFrequency (%)
0 104032
78.9%
1 4799
 
3.6%
2 4176
 
3.2%
3 7460
 
5.7%
4 5154
 
3.9%
5 6293
 
4.8%
ValueCountFrequency (%)
5 6293
 
4.8%
4 5154
 
3.9%
3 7460
 
5.7%
2 4176
 
3.2%
1 4799
 
3.6%
0 104032
78.9%

Work Pressure
Real number (ℝ)

High correlation  Zeros 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3412375
Minimum0
Maximum5
Zeros27882
Zeros (%)21.1%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2024-12-07T22:25:04.231969image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7361132
Coefficient of variation (CV)0.74153658
Kurtosis-1.2869615
Mean2.3412375
Median Absolute Deviation (MAD)2
Skewness0.093443828
Sum308842
Variance3.0140891
MonotonicityNot monotonic
2024-12-07T22:25:04.313815image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 27882
21.1%
2 22981
17.4%
4 20697
15.7%
3 20297
15.4%
1 20271
15.4%
5 19786
15.0%
ValueCountFrequency (%)
0 27882
21.1%
1 20271
15.4%
2 22981
17.4%
3 20297
15.4%
4 20697
15.7%
5 19786
15.0%
ValueCountFrequency (%)
5 19786
15.0%
4 20697
15.7%
3 20297
15.4%
2 22981
17.4%
1 20271
15.4%
0 27882
21.1%

CGPA
Real number (ℝ)

High correlation 

Distinct332
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.83018044
Minimum-1
Maximum10
Zeros0
Zeros (%)0.0%
Negative104032
Negative (%)78.9%
Memory size2.0 MiB
2024-12-07T22:25:04.405813image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median-1
Q3-1
95-th percentile8.95
Maximum10
Range11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.5987633
Coefficient of variation (CV)4.3349171
Kurtosis0.55137079
Mean0.83018044
Median Absolute Deviation (MAD)0
Skewness1.5385463
Sum109512.42
Variance12.951098
MonotonicityNot monotonic
2024-12-07T22:25:04.636479image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1 104032
78.9%
8.04 821
 
0.6%
9.96 425
 
0.3%
5.74 409
 
0.3%
8.95 371
 
0.3%
9.21 343
 
0.3%
7.25 339
 
0.3%
7.09 320
 
0.2%
7.88 318
 
0.2%
9.44 317
 
0.2%
Other values (322) 24219
 
18.4%
ValueCountFrequency (%)
-1 104032
78.9%
5.03 17
 
< 0.1%
5.06 15
 
< 0.1%
5.08 95
 
0.1%
5.09 20
 
< 0.1%
5.1 66
 
0.1%
5.11 112
 
0.1%
5.12 167
 
0.1%
5.14 19
 
< 0.1%
5.16 209
 
0.2%
ValueCountFrequency (%)
10 58
 
< 0.1%
9.98 59
 
< 0.1%
9.97 139
 
0.1%
9.96 425
0.3%
9.95 133
 
0.1%
9.94 71
 
0.1%
9.93 274
0.2%
9.92 60
 
< 0.1%
9.91 123
 
0.1%
9.9 28
 
< 0.1%

Study Satisfaction
Real number (ℝ)

High correlation  Zeros 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.62245857
Minimum0
Maximum5
Zeros104032
Zeros (%)78.9%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2024-12-07T22:25:04.713764image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.3552482
Coefficient of variation (CV)2.1772505
Kurtosis3.0073309
Mean0.62245857
Median Absolute Deviation (MAD)0
Skewness2.0844162
Sum82111
Variance1.8366977
MonotonicityNot monotonic
2024-12-07T22:25:04.785758image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 104032
78.9%
4 6358
 
4.8%
2 5837
 
4.4%
3 5819
 
4.4%
1 5448
 
4.1%
5 4420
 
3.4%
ValueCountFrequency (%)
0 104032
78.9%
1 5448
 
4.1%
2 5837
 
4.4%
3 5819
 
4.4%
4 6358
 
4.8%
5 4420
 
3.4%
ValueCountFrequency (%)
5 4420
 
3.4%
4 6358
 
4.8%
3 5819
 
4.4%
2 5837
 
4.4%
1 5448
 
4.1%
0 104032
78.9%

Job Satisfaction
Real number (ℝ)

High correlation  Zeros 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.344861
Minimum0
Maximum5
Zeros27882
Zeros (%)21.1%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2024-12-07T22:25:04.863785image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7444378
Coefficient of variation (CV)0.74394078
Kurtosis-1.2855065
Mean2.344861
Median Absolute Deviation (MAD)2
Skewness0.10473503
Sum309320
Variance3.0430631
MonotonicityNot monotonic
2024-12-07T22:25:04.941819image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 27882
21.1%
2 22978
17.4%
5 20850
15.8%
3 20592
15.6%
1 20370
15.4%
4 19242
14.6%
ValueCountFrequency (%)
0 27882
21.1%
1 20370
15.4%
2 22978
17.4%
3 20592
15.6%
4 19242
14.6%
5 20850
15.8%
ValueCountFrequency (%)
5 20850
15.8%
4 19242
14.6%
3 20592
15.6%
2 22978
17.4%
1 20370
15.4%
0 27882
21.1%

Sleep Duration
Categorical

Imbalance 

Distinct35
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
Less than 5 hours
35864 
7-8 hours
34916 
More than 8 hours
30822 
5-6 hours
30235 
3-4 hours
 
11
Other values (30)
 
66

Length

Max length17
Median length17
Mean length13.044029
Min length2

Characters and Unicode

Total characters1720690
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)< 0.1%

Sample

1st rowMore than 8 hours
2nd rowLess than 5 hours
3rd row5-6 hours
4th rowLess than 5 hours
5th row5-6 hours

Common Values

ValueCountFrequency (%)
Less than 5 hours 35864
27.2%
7-8 hours 34916
26.5%
More than 8 hours 30822
23.4%
5-6 hours 30235
22.9%
3-4 hours 11
 
< 0.1%
6-7 hours 8
 
< 0.1%
4-5 hours 7
 
< 0.1%
4-6 hours 5
 
< 0.1%
2-3 hours 5
 
< 0.1%
No 4
 
< 0.1%
Other values (25) 37
 
< 0.1%

Length

2024-12-07T22:25:05.040132image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
hours 131899
33.2%
than 66687
16.8%
5 35865
 
9.0%
less 35864
 
9.0%
7-8 34916
 
8.8%
8 30823
 
7.8%
more 30822
 
7.8%
5-6 30235
 
7.6%
3-4 11
 
< 0.1%
6-7 8
 
< 0.1%
Other values (26) 56
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
265272
15.4%
s 203628
11.8%
h 198590
11.5%
o 162731
9.5%
r 162727
9.5%
u 131904
7.7%
e 66696
 
3.9%
t 66693
 
3.9%
n 66693
 
3.9%
a 66692
 
3.9%
Other values (28) 329064
19.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1720690
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
265272
15.4%
s 203628
11.8%
h 198590
11.5%
o 162731
9.5%
r 162727
9.5%
u 131904
7.7%
e 66696
 
3.9%
t 66693
 
3.9%
n 66693
 
3.9%
a 66692
 
3.9%
Other values (28) 329064
19.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1720690
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
265272
15.4%
s 203628
11.8%
h 198590
11.5%
o 162731
9.5%
r 162727
9.5%
u 131904
7.7%
e 66696
 
3.9%
t 66693
 
3.9%
n 66693
 
3.9%
a 66692
 
3.9%
Other values (28) 329064
19.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1720690
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
265272
15.4%
s 203628
11.8%
h 198590
11.5%
o 162731
9.5%
r 162727
9.5%
u 131904
7.7%
e 66696
 
3.9%
t 66693
 
3.9%
n 66693
 
3.9%
a 66692
 
3.9%
Other values (28) 329064
19.1%

Dietary Habits
Categorical

Imbalance 

Distinct23
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
Moderate
46883 
Unhealthy
42788 
Healthy
42221 
Yes
 
2
No
 
2
Other values (18)
 
18

Length

Max length17
Median length12
Mean length8.0039874
Min length1

Characters and Unicode

Total characters1055838
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18 ?
Unique (%)< 0.1%

Sample

1st rowHealthy
2nd rowUnhealthy
3rd rowHealthy
4th rowModerate
5th rowUnhealthy

Common Values

ValueCountFrequency (%)
Moderate 46883
35.5%
Unhealthy 42788
32.4%
Healthy 42221
32.0%
Yes 2
 
< 0.1%
No 2
 
< 0.1%
Hormonal 1
 
< 0.1%
Pratham 1
 
< 0.1%
BSc 1
 
< 0.1%
Gender 1
 
< 0.1%
3 1
 
< 0.1%
Other values (13) 13
 
< 0.1%

Length

2024-12-07T22:25:05.149813image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
moderate 46883
35.5%
unhealthy 42788
32.4%
healthy 42225
32.0%
no 3
 
< 0.1%
less 2
 
< 0.1%
yes 2
 
< 0.1%
hormonal 1
 
< 0.1%
m.tech 1
 
< 0.1%
electrician 1
 
< 0.1%
12 1
 
< 0.1%
Other values (13) 13
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 178790
16.9%
a 131904
12.5%
t 131899
12.5%
h 127805
12.1%
l 85017
8.1%
y 85013
8.1%
o 46891
 
4.4%
r 46890
 
4.4%
M 46887
 
4.4%
d 46885
 
4.4%
Other values (26) 127857
12.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1055838
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 178790
16.9%
a 131904
12.5%
t 131899
12.5%
h 127805
12.1%
l 85017
8.1%
y 85013
8.1%
o 46891
 
4.4%
r 46890
 
4.4%
M 46887
 
4.4%
d 46885
 
4.4%
Other values (26) 127857
12.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1055838
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 178790
16.9%
a 131904
12.5%
t 131899
12.5%
h 127805
12.1%
l 85017
8.1%
y 85013
8.1%
o 46891
 
4.4%
r 46890
 
4.4%
M 46887
 
4.4%
d 46885
 
4.4%
Other values (26) 127857
12.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1055838
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 178790
16.9%
a 131904
12.5%
t 131899
12.5%
h 127805
12.1%
l 85017
8.1%
y 85013
8.1%
o 46891
 
4.4%
r 46890
 
4.4%
M 46887
 
4.4%
d 46885
 
4.4%
Other values (26) 127857
12.1%

Degree
Text

Distinct112
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
2024-12-07T22:25:05.442785image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length20
Median length19
Mean length4.1477554
Min length1

Characters and Unicode

Total characters547147
Distinct characters56
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique69 ?
Unique (%)0.1%

Sample

1st rowBHM
2nd rowLLB
3rd rowB.Pharm
4th rowBBA
5th rowBBA
ValueCountFrequency (%)
b.ed 11680
 
8.5%
b.arch 8729
 
6.3%
b.com 8107
 
5.9%
class 6130
 
4.4%
12 6129
 
4.4%
b.pharm 5851
 
4.2%
bca 5728
 
4.1%
m.ed 5662
 
4.1%
mca 5225
 
3.8%
bba 5026
 
3.6%
Other values (105) 69791
50.6%
2024-12-07T22:25:05.867018image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
B 85058
15.5%
M 60141
 
11.0%
. 56567
 
10.3%
A 35168
 
6.4%
h 31136
 
5.7%
C 28285
 
5.2%
c 27532
 
5.0%
E 24075
 
4.4%
m 21587
 
3.9%
r 19146
 
3.5%
Other values (46) 158452
29.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 547147
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B 85058
15.5%
M 60141
 
11.0%
. 56567
 
10.3%
A 35168
 
6.4%
h 31136
 
5.7%
C 28285
 
5.2%
c 27532
 
5.0%
E 24075
 
4.4%
m 21587
 
3.9%
r 19146
 
3.5%
Other values (46) 158452
29.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 547147
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B 85058
15.5%
M 60141
 
11.0%
. 56567
 
10.3%
A 35168
 
6.4%
h 31136
 
5.7%
C 28285
 
5.2%
c 27532
 
5.0%
E 24075
 
4.4%
m 21587
 
3.9%
r 19146
 
3.5%
Other values (46) 158452
29.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 547147
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B 85058
15.5%
M 60141
 
11.0%
. 56567
 
10.3%
A 35168
 
6.4%
h 31136
 
5.7%
C 28285
 
5.2%
c 27532
 
5.0%
E 24075
 
4.4%
m 21587
 
3.9%
r 19146
 
3.5%
Other values (46) 158452
29.0%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
False
67407 
True
64507 
ValueCountFrequency (%)
False 67407
51.1%
True 64507
48.9%
2024-12-07T22:25:05.975113image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Work/Study Hours
Real number (ℝ)

Zeros 

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2408008
Minimum0
Maximum12
Zeros11331
Zeros (%)8.6%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2024-12-07T22:25:06.049628image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median6
Q310
95-th percentile12
Maximum12
Range12
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.8563405
Coefficient of variation (CV)0.61792398
Kurtosis-1.2884184
Mean6.2408008
Median Absolute Deviation (MAD)4
Skewness-0.12371745
Sum823249
Variance14.871362
MonotonicityNot monotonic
2024-12-07T22:25:06.146830image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
10 13337
10.1%
11 12020
9.1%
9 11948
9.1%
0 11331
8.6%
12 10619
8.0%
2 9972
 
7.6%
6 9797
 
7.4%
1 9292
 
7.0%
7 8955
 
6.8%
3 8922
 
6.8%
Other values (3) 25721
19.5%
ValueCountFrequency (%)
0 11331
8.6%
1 9292
7.0%
2 9972
7.6%
3 8922
6.8%
4 8575
6.5%
5 8746
6.6%
6 9797
7.4%
7 8955
6.8%
8 8400
6.4%
9 11948
9.1%
ValueCountFrequency (%)
12 10619
8.0%
11 12020
9.1%
10 13337
10.1%
9 11948
9.1%
8 8400
6.4%
7 8955
6.8%
6 9797
7.4%
5 8746
6.6%
4 8575
6.5%
3 8922
6.8%

Financial Stress
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
2.0
29664 
5.0
26208 
4.0
25906 
1.0
25681 
3.0
24455 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters395742
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row3.0
3rd row1.0
4th row1.0
5th row4.0

Common Values

ValueCountFrequency (%)
2.0 29664
22.5%
5.0 26208
19.9%
4.0 25906
19.6%
1.0 25681
19.5%
3.0 24455
18.5%

Length

2024-12-07T22:25:06.238969image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-07T22:25:06.323029image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
2.0 29664
22.5%
5.0 26208
19.9%
4.0 25906
19.6%
1.0 25681
19.5%
3.0 24455
18.5%

Most occurring characters

ValueCountFrequency (%)
. 131914
33.3%
0 131914
33.3%
2 29664
 
7.5%
5 26208
 
6.6%
4 25906
 
6.5%
1 25681
 
6.5%
3 24455
 
6.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 395742
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 131914
33.3%
0 131914
33.3%
2 29664
 
7.5%
5 26208
 
6.6%
4 25906
 
6.5%
1 25681
 
6.5%
3 24455
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 395742
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 131914
33.3%
0 131914
33.3%
2 29664
 
7.5%
5 26208
 
6.6%
4 25906
 
6.5%
1 25681
 
6.5%
3 24455
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 395742
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 131914
33.3%
0 131914
33.3%
2 29664
 
7.5%
5 26208
 
6.6%
4 25906
 
6.5%
1 25681
 
6.5%
3 24455
 
6.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
False
66272 
True
65642 
ValueCountFrequency (%)
False 66272
50.2%
True 65642
49.8%
2024-12-07T22:25:06.408920image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Depression
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
0
109646 
1
22268 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters131914
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 109646
83.1%
1 22268
 
16.9%

Length

2024-12-07T22:25:06.491922image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-07T22:25:06.564727image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 109646
83.1%
1 22268
 
16.9%

Most occurring characters

ValueCountFrequency (%)
0 109646
83.1%
1 22268
 
16.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 131914
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 109646
83.1%
1 22268
 
16.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 131914
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 109646
83.1%
1 22268
 
16.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 131914
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 109646
83.1%
1 22268
 
16.9%

Interactions

2024-12-07T22:24:59.347635image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2024-12-07T22:24:58.528875image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2024-12-07T22:24:55.455661image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2024-12-07T22:24:56.754446image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-07T22:24:57.409413image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-07T22:24:58.051574image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-07T22:24:58.856395image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2024-12-07T22:24:55.849636image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-07T22:24:56.505283image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2024-12-07T22:24:59.260605image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-12-07T22:25:06.635063image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Academic PressureAgeCGPADepressionDietary HabitsFamily History of Mental IllnessFinancial StressGenderHave you ever had suicidal thoughts ?Job SatisfactionSleep DurationStudy SatisfactionWork PressureWork/Study HoursWorking Professional or Studentid
Academic Pressure1.000-0.6260.9810.6450.0360.0190.0520.0120.191-0.7110.0210.980-0.7110.1281.0000.001
Age-0.6261.000-0.6240.6270.0270.0150.0520.0530.1750.4670.023-0.6240.422-0.1120.7690.001
CGPA0.981-0.6241.0000.5770.0300.0130.0350.0230.149-0.7110.0200.981-0.7110.1221.0000.000
Depression0.6450.6270.5771.0000.1440.0160.2230.0070.3370.5840.0830.5850.5860.2030.5760.000
Dietary Habits0.0360.0270.0300.1441.0000.0000.0250.0390.0610.0280.0000.0310.0290.0110.0600.000
Family History of Mental Illness0.0190.0150.0130.0160.0001.0000.0130.0160.0080.0170.0030.0140.0160.0150.0140.003
Financial Stress0.0520.0520.0350.2230.0250.0131.0000.0110.0860.0420.0190.0390.0370.0230.0700.000
Gender0.0120.0530.0230.0070.0390.0160.0111.0000.0110.0160.0010.0140.0050.0150.0070.006
Have you ever had suicidal thoughts ?0.1910.1750.1490.3370.0610.0080.0860.0111.0000.1550.0310.1530.1520.0640.1490.000
Job Satisfaction-0.7110.467-0.7110.5840.0280.0170.0420.0160.1551.0000.016-0.7110.500-0.1031.0000.000
Sleep Duration0.0210.0230.0200.0830.0000.0030.0190.0010.0310.0161.0000.0180.0160.0130.0340.000
Study Satisfaction0.980-0.6240.9810.5850.0310.0140.0390.0140.153-0.7110.0181.000-0.7110.1201.0000.001
Work Pressure-0.7110.422-0.7110.5860.0290.0160.0370.0050.1520.5000.016-0.7111.000-0.0941.0000.001
Work/Study Hours0.128-0.1120.1220.2030.0110.0150.0230.0150.064-0.1030.0130.120-0.0941.0000.1470.003
Working Professional or Student1.0000.7691.0000.5760.0600.0140.0700.0070.1491.0000.0341.0001.0000.1471.0000.000
id0.0010.0010.0000.0000.0000.0030.0000.0060.0000.0000.0000.0010.0010.0030.0001.000

Missing values

2024-12-07T22:25:00.104544image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-12-07T22:25:00.451804image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

idNameGenderAgeCityWorking Professional or StudentProfessionAcademic PressureWork PressureCGPAStudy SatisfactionJob SatisfactionSleep DurationDietary HabitsDegreeHave you ever had suicidal thoughts ?Work/Study HoursFinancial StressFamily History of Mental IllnessDepression
00AaradhyaFemale49.0LudhianaWorking ProfessionalChef0.05.0-1.000.02.0More than 8 hoursHealthyBHMNo1.02.0No0
11VivanMale26.0VaranasiWorking ProfessionalTeacher0.04.0-1.000.03.0Less than 5 hoursUnhealthyLLBYes7.03.0No1
22YuvrajMale33.0VisakhapatnamStudentStudent5.00.08.972.00.05-6 hoursHealthyB.PharmYes3.01.0No1
33YuvrajMale22.0MumbaiWorking ProfessionalTeacher0.05.0-1.000.01.0Less than 5 hoursModerateBBAYes10.01.0Yes1
44RheaFemale30.0KanpurWorking ProfessionalBusiness Analyst0.01.0-1.000.01.05-6 hoursUnhealthyBBAYes9.04.0Yes0
55VaniFemale59.0AhmedabadWorking ProfessionalFinanancial Analyst0.02.0-1.000.05.05-6 hoursHealthyMCANo7.05.0No0
66RitvikMale47.0ThaneWorking ProfessionalChemist0.05.0-1.000.02.07-8 hoursModerateMDNo6.02.0No0
77RajveerMale38.0NashikWorking ProfessionalTeacher0.03.0-1.000.04.07-8 hoursUnhealthyB.PharmNo10.03.0Yes0
88AishwaryaFemale24.0BangaloreStudentStudent2.00.05.905.00.05-6 hoursModerateBScNo3.02.0Yes0
99SimranFemale42.0PatnaWorking ProfessionalElectrician0.04.0-1.000.01.05-6 hoursHealthyMEYes7.02.0Yes0
idNameGenderAgeCityWorking Professional or StudentProfessionAcademic PressureWork PressureCGPAStudy SatisfactionJob SatisfactionSleep DurationDietary HabitsDegreeHave you ever had suicidal thoughts ?Work/Study HoursFinancial StressFamily History of Mental IllnessDepression
140689140689AyaanMale31.0FaridabadStudentStudent3.00.06.614.00.05-6 hoursUnhealthyMDNo12.02.0No0
140690140690RashiFemale18.0LudhianaStudentStudent5.00.06.882.00.0Less than 5 hoursHealthyClass 12Yes10.05.0No1
140691140691ZaraFemale57.0MeerutWorking ProfessionalTeacher0.01.0-1.000.01.0Less than 5 hoursModerateB.ArchYes4.05.0Yes0
140692140692RaunakMale49.0BhopalWorking ProfessionalFinancial Analyst0.04.0-1.000.01.07-8 hoursModerateMBANo9.01.0No0
140693140693ShauryaMale55.0SrinagarWorking ProfessionalData Scientist0.01.0-1.000.03.0Less than 5 hoursUnhealthyM.TechNo9.02.0No0
140694140694IshaaniFemale45.0AhmedabadWorking ProfessionalTeacher0.02.0-1.000.05.0Less than 5 hoursModerateB.EdYes1.05.0No0
140696140696LataFemale41.0HyderabadWorking ProfessionalContent Writer0.05.0-1.000.04.07-8 hoursModerateB.TechYes6.05.0Yes0
140697140697AanchalFemale24.0KolkataWorking ProfessionalMarketing Manager0.03.0-1.000.01.0More than 8 hoursModerateB.ComNo4.04.0No0
140698140698PrachiFemale49.0SrinagarWorking ProfessionalPlumber0.05.0-1.000.02.05-6 hoursModerateMEYes10.01.0No0
140699140699SaiMale27.0PatnaStudentStudent4.00.09.241.00.0Less than 5 hoursHealthyBCAYes2.03.0Yes1